\section{Conclusion}

In this section follows a summary of the project and what conclusions may be drawn from its performance. In the preceding chapters we have described our implementation of the heat kernel signature algorithm as given in the provided material. The implementation performs well time-wise: the set of all computations can be completed in less than a day. The program works in several steps, and partial results can be efficiently stored so that certain steps may be repeated using slightly different methods, with no need for complete recomputation.

Moving on, the most important measure of the project's success is of course its final result. How well does our implementation of the heat kernel signature method perform in matching similar model files? As stated in the \textit{Performance} section of this report, good results have been obtained under isometry, topology, noise, shot noise, micro-holes and partial transformations. Furthermore, somewhat positive results have been obtained under rasterize and scale transformations. However, the implementation does not perform well under holes, sampling, view and affine transformations. On top of this, the various single number measures we employed indicate that the implementation significantly outperforms  the random method in all cases. 

The only real sticking point is the program's performance for null models 8-15, which did not seem to match up at all. The most likely explanation is that for these groups the null model itself was not used as the base for the various transformations, so that the transformed models do not provide good matches. We demonstrated the program worked as expected when using the micro-holes models. Apart from the issue that was just described the program's performance is satisfactory. In conclusion, we believe our program to be a correct implementation of the provided material, as this report illustrates.

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